{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes:\n",
    " * same as 6-2 but with no TOI data in training or validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/1/AstroCNNModelVetting_vrevised_tuned_20211023_235405',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/2/AstroCNNModelVetting_vrevised_tuned_20211024_001859',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/3/AstroCNNModelVetting_vrevised_tuned_20211024_004345',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/4/AstroCNNModelVetting_vrevised_tuned_20211024_010836',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/5/AstroCNNModelVetting_vrevised_tuned_20211024_013348',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/6/AstroCNNModelVetting_vrevised_tuned_20211024_020004',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/7/AstroCNNModelVetting_vrevised_tuned_20211024_022515',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/8/AstroCNNModelVetting_vrevised_tuned_20211024_025017',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/9/AstroCNNModelVetting_vrevised_tuned_20211024_031517',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/10/AstroCNNModelVetting_vrevised_tuned_20211024_034009']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "chkpt_root = '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2'\n",
    "data_files = '/mnt/tess/astronet/tfrecords-vetting-6-val/*'\n",
    "tces_file = '/mnt/tess/astronet/tces-vetting-v5-val.csv'\n",
    "\n",
    "nruns = 10\n",
    "\n",
    "def load_ensemble(chkpt_root, nruns):\n",
    "    checkpts = []\n",
    "    for i in range(nruns):\n",
    "        parent = os.path.join(chkpt_root, str(i + 1))\n",
    "        if not os.path.exists(parent):\n",
    "            break\n",
    "        all_dirs = os.listdir(parent)\n",
    "        if not all_dirs:\n",
    "            break\n",
    "        d, = all_dirs\n",
    "        checkpts.append(os.path.join(parent, d))\n",
    "    return checkpts\n",
    "\n",
    "paths = load_ensemble(chkpt_root, nruns)\n",
    "paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running model 1\n",
      "231 records\n",
      "Running model 2\n",
      "231 records\n",
      "Running model 3\n",
      "231 records\n",
      "Running model 4\n",
      "231 records\n",
      "Running model 5\n",
      "231 records\n",
      "Running model 6\n",
      "231 records\n",
      "Running model 7\n",
      "231 records\n",
      "Running model 8\n",
      "231 records\n",
      "Running model 9\n",
      "231 records\n",
      "Running model 10\n",
      "231 records\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "import os\n",
    "from astronet import predict\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def run_predictions(path):\n",
    "    predict.FLAGS = predict.parser.parse_args([\n",
    "      '--model_dir', path,\n",
    "      '--data_files', data_files,\n",
    "      '--output_file', '',\n",
    "    ])\n",
    "\n",
    "    return predict.predict()\n",
    "\n",
    "\n",
    "paths = load_ensemble(chkpt_root, nruns)\n",
    "ensemble_preds = []\n",
    "config = None\n",
    "for i, path in enumerate(paths):\n",
    "    print(f'Running model {i + 1}')\n",
    "    preds, config = run_predictions(path)\n",
    "    ensemble_preds.append(preds.set_index('tic_id'))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['disp_p', 'disp_e', 'disp_n']\n",
    "\n",
    "col_p = labels.index('disp_p')\n",
    "thresh = 0.1579816417694639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "agg_preds = {}\n",
    "\n",
    "for preds in ensemble_preds:\n",
    "    for tic_id in preds.index:\n",
    "        if tic_id not in agg_preds:\n",
    "            agg_preds[tic_id] = []\n",
    "\n",
    "        row = preds[preds.index == tic_id]\n",
    "        pred_v = row.values[0]\n",
    "        if len(row.values) > 1:\n",
    "            print(f'Warning: duplicate predictions for {tic_id}')\n",
    "        if pred_v[col_p] >= thresh:\n",
    "            agg_preds[tic_id].append('disp_p')\n",
    "        else:\n",
    "            agg_preds[tic_id].append(preds.columns[np.argmax(pred_v)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_preds = []\n",
    "for tic_id in list(agg_preds.keys()):\n",
    "    counts = {l: 0 for l in labels}\n",
    "    for e in agg_preds[tic_id]:\n",
    "        counts[e] += 1\n",
    "    maxcount = max(counts.values())\n",
    "    counts.update({\n",
    "        'tic_id': tic_id,\n",
    "        'maxcount': maxcount,\n",
    "    })\n",
    "    final_preds.append(counts)\n",
    "    \n",
    "final_preds = pd.DataFrame(final_preds).set_index('tic_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tce_table = pd.read_csv(tces_file, header=0).set_index('tic_id')\n",
    "tce_labels = tce_table[labels]\n",
    "\n",
    "pl = final_preds.join(tce_labels, on='tic_id', how='left', lsuffix='_p')\n",
    "\n",
    "pl.head()\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.8947368421052632\n",
      "Precision: 0.3469387755102041\n"
     ]
    }
   ],
   "source": [
    "ppos = (pl['disp_p_p'] > 0)\n",
    "pos = (pl['disp_p'] > 0)\n",
    "\n",
    "pneg = (pl['disp_p_p'] == 0)\n",
    "neg = (pl['disp_p'] == 0)\n",
    "\n",
    "print('Recall:', len(pl[ppos & pos]) / len(pl[pos]))\n",
    "print('Precision:', len(pl[ppos & pos]) / len(pl[ppos]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29984131\n",
      "283156726\n"
     ]
    }
   ],
   "source": [
    "for i in pl[pos & pneg][[]].join(tce_table, on='tic_id', how='left').index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119774374\n",
      "23826331\n",
      "24491836\n",
      "276645751\n",
      "276936320\n",
      "278225267\n",
      "278866211\n",
      "280095254\n",
      "30275619\n",
      "304729846\n",
      "307153959\n",
      "344978642\n",
      "380783252\n",
      "384068226\n",
      "384744828\n",
      "424876542\n",
      "98350979\n",
      "142963071\n",
      "137778682\n",
      "365494323\n",
      "388076435\n",
      "421154783\n",
      "149854222\n",
      "326974824\n",
      "262173985\n",
      "321828403\n",
      "24004619\n",
      "457138169\n",
      "255994336\n",
      "1715469662\n",
      "1715469667\n",
      "272783172\n"
     ]
    }
   ],
   "source": [
    "for i in pl[neg & ppos][[]].join(tce_table, on='tic_id', how='left').index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_p</th>\n",
       "      <th>disp_e</th>\n",
       "      <th>disp_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.934916</td>\n",
       "      <td>0.049846</td>\n",
       "      <td>0.015237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.912334</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.006362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.648020</td>\n",
       "      <td>0.336243</td>\n",
       "      <td>0.015737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.892884</td>\n",
       "      <td>0.090375</td>\n",
       "      <td>0.016741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.938803</td>\n",
       "      <td>0.049757</td>\n",
       "      <td>0.011440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.878660</td>\n",
       "      <td>0.114032</td>\n",
       "      <td>0.007308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.882939</td>\n",
       "      <td>0.100658</td>\n",
       "      <td>0.016403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.756971</td>\n",
       "      <td>0.223800</td>\n",
       "      <td>0.019230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.929319</td>\n",
       "      <td>0.055864</td>\n",
       "      <td>0.014817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.936495</td>\n",
       "      <td>0.047254</td>\n",
       "      <td>0.016251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             disp_p    disp_e    disp_n\n",
       "tic_id                                 \n",
       "113428669  0.934916  0.049846  0.015237\n",
       "113428669  0.912334  0.081304  0.006362\n",
       "113428669  0.648020  0.336243  0.015737\n",
       "113428669  0.892884  0.090375  0.016741\n",
       "113428669  0.938803  0.049757  0.011440\n",
       "113428669  0.878660  0.114032  0.007308\n",
       "113428669  0.882939  0.100658  0.016403\n",
       "113428669  0.756971  0.223800  0.019230\n",
       "113428669  0.929319  0.055864  0.014817\n",
       "113428669  0.936495  0.047254  0.016251"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compare(ensemble_preds, filter):\n",
    "    result = ensemble_preds[0][filter]\n",
    "    for preds in ensemble_preds[1:]:\n",
    "        result = result.append(preds[filter])\n",
    "    return result\n",
    "\n",
    "compare(ensemble_preds, preds.index == 113428669)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_p_p</th>\n",
       "      <th>disp_e_p</th>\n",
       "      <th>disp_n_p</th>\n",
       "      <th>maxcount</th>\n",
       "      <th>disp_p</th>\n",
       "      <th>disp_e</th>\n",
       "      <th>disp_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           disp_p_p  disp_e_p  disp_n_p  maxcount  disp_p  disp_e  disp_n\n",
       "tic_id                                                                   \n",
       "113428669        10         0         0        10       1       0       0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[pl.index == 113428669]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tics = set(ensemble_preds[0].index.values)\n",
    "\n",
    "tic_index = {v: i for i, v in enumerate(tics)}\n",
    "\n",
    "pred_ps = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "pred_es = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "pred_ns = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "for i, preds in enumerate(ensemble_preds):\n",
    "    for row in preds.iterrows():\n",
    "        tic, pred_p = row[0], row[1][col_p]\n",
    "        pred_ps[i][tic_index[tic]] = pred_p\n",
    "        pred_es[i][tic_index[tic]] = row[1][labels.index('disp_e')]\n",
    "        pred_ns[i][tic_index[tic]] = row[1][labels.index('disp_n')]\n",
    "\n",
    "lbl_ps = np.zeros([len(tic_index)], dtype=np.bool)\n",
    "bad_labels = []\n",
    "for row in tce_labels.iterrows():\n",
    "    tic, lbl_p = row[0], row[1]['disp_p']\n",
    "    if tic in tic_index:\n",
    "        lbl_ps[tic_index[tic]] = (lbl_p > 0)\n",
    "    else:\n",
    "        bad_labels.append(tic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cond_pos = float(np.sum(lbl_ps))\n",
    "\n",
    "def pr_at_th(th):\n",
    "    pred_pos = np.any(pred_ps >= th, axis=0)\n",
    "    true_pos = pred_pos & lbl_ps\n",
    "    num_pred_pos = int(np.sum(pred_pos))\n",
    "    num_true_pos = int(np.sum(true_pos))\n",
    "    if num_pred_pos == 0:\n",
    "        return 1.0, 0.0\n",
    "    return float(num_true_pos) / float(num_pred_pos), float(num_true_pos) / float(num_cond_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.4864421079468777, max R: 1.0, max P: 1.0\n",
      "100% recall at: 32%, threshold: 0.077172879695944\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x740 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "ps, rs, ths = ([], [], [])\n",
    "th = np.max(pred_ps)\n",
    "while th >= 0.0:\n",
    "    p, r = pr_at_th(th)\n",
    "    if p > 0:\n",
    "        ps.append(p)\n",
    "        rs.append(r)\n",
    "    else:\n",
    "        ps.append(1.0)\n",
    "        rs.append(0.0)\n",
    "    ths.append(th)\n",
    "    th -= 0.0005\n",
    "    \n",
    "from sklearn import metrics\n",
    "\n",
    "print(f'AUC: {metrics.auc(rs, ps)}, max R: {max(rs)}, max P: {max(ps)}')\n",
    "    \n",
    "i = len(rs) - 1\n",
    "while rs[i] == 1.0:\n",
    "    i -= 1\n",
    "i += 1\n",
    "print(f'100% recall at: {int(ps[i] * 100)}%, threshold: {ths[i]}')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 3.7), dpi=200)\n",
    "\n",
    "ax.spines['top'].set_color('#808080')\n",
    "ax.spines['right'].set_color('#808080')\n",
    "ax.spines['left'].set_color('#808080')\n",
    "ax.spines['bottom'].set_color('#808080')\n",
    "ax.tick_params(direction='in', color='#808080')\n",
    "\n",
    "plt.grid(color='#c0c0c0', linestyle='--', linewidth=0.5)\n",
    "\n",
    "plt.ylabel('Precision', fontweight='bold')\n",
    "plt.xlabel('Recall', fontweight='bold')\n",
    "\n",
    "plt.xlim((0.0, 1.0))\n",
    "plt.ylim((0.0, 1.0))\n",
    "\n",
    "_ = plt.plot(rs, ps, marker='.')\n",
    "_ = plt.plot([0.0, 1.0], [1.0, 0.0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
